Papers
22
Total Citations
335
H-Index
9
About
Thomas Wiedemann is a robotics and autonomous systems researcher whose work sits at the intersection of multi-robot coordination, probabilistic modeling, and environmental sensing. He is best known for pioneering contributions to decentralized multi-agent exploration, where his 2016 paper on online Gaussian process learning for cooperative exploration has accumulated 63 citations and established a foundational framework for intelligent, data-efficient sampling in unknown environments. A recurring theme across his portfolio is gas source localization — the autonomous detection of hazardous leaks in disaster scenarios — where he has advanced both model-based probabilistic strategies incorporating partial differential equations and reinforcement learning approaches augmented by domain knowledge. His 2020 work on self-aware swarm navigation, with 62 citations, further demonstrates his influence in scalable, resilient multi-robot systems for remote sensing missions. Wiedemann consistently bridges theoretical rigor with experimental validation, addressing real-world challenges such as model mismatch, advection-diffusion dynamics, and ventilation characterization. With over 290 cumulative citations across a focused and coherent body of work, he has made a meaningful impact on the safety robotics community, offering practical tools for reducing human exposure to dangerous environments through intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2Self-Aware Swarm Navigation in Autonomous Exploration Missions62 citations · 2020
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